CN105654482A - Digital image technology-based soil-rock mixture representative element volume size determining method - Google Patents
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Abstract
The invention discloses a digital image technology-based soil-rock mixture representative element volume size determining method. The method includes the following steps that: denoising and binarization are performed on an obtained color image, and a binary image representing soil and rocks is obtained; size levels are determined according to the size of the original image, and binary images of different sizes are obtained; graphic boundaries in each image are extracted and are subjected to smoothing processing, so that a file for finite-element mesh division can be obtained, and the obtained file is converted into a numerical value computation model, and a numerical value test is carried out, so that a corresponding equivalent macroscopic mechanical parameter can be obtained; and test results are settled, and the REV (representative element volume) size of a soil-rock mixture is determined according to accuracy requirements. With the method of the invention adopted, a defect that a traditional cellular automation simulation-based soil-rock mixture method cannot represent the boundary shape of block rock particles and a defect that a traditional stochastic model cannot reflect actual soil-rock distribution can be eliminated, and the obtained REV size of the soil-rock mixture fully considers the characteristics of a field soil-rock mixture. The method has the advantages of high efficiency, high precision, low cost and the like.
Description
Technical Field
The invention relates to a method for determining the dimension of a soil-rock mixture characterization unit body, in particular to a method for determining the dimension of the soil-rock mixture characterization unit body based on a digital image technology.
Background
The soil-rock mixture is a mixture of soil and broken stones. Most of the naturally formed soil-rock mixtures are residual slope deposits, collapse slope deposits, flood deposits and the like, and are widely distributed in three gorges reservoir areas, Qinghai-Tibet plateau in Sichuan and West, Fujian in the southeast coast, hong Kong and the like. A large amount of soil and stone mixture exists in an earth and stone dam, a tailing dam generated by mining, a dumping site, a broken stone foundation, a roadbed and the like in the engineering construction process. These earth and rock mixtures are geological carriers which are frequently encountered in the field of geotechnical engineering and which must be handled properly. The soil-rock mixture is used as a mixture of a soil body and crushed rocks and is a special engineering geological material between a homogeneous soil body and a cracked rock body. The macroscopic mechanical property of the soil-rock mixture not only depends on the mechanical property of soil and rock, but also is closely related to the relative proportion of soil and rock, the shape of broken stone and the like. Therefore, the equivalent mechanical parameters of the soil-rock mixture are reasonably determined, and the method has very important significance for the design, construction, deformation prediction and stability evaluation of geotechnical engineering.
The equivalent parameters of the soil-rock mixture have obvious size effect. Namely, the calculated value or the experimental value of the equivalent parameter of the earth-rock mixture changes with the change of the model scale of the earth-rock mixture, but when the scale is increased to a certain critical value, the equivalent parameter approaches to a constant, and the critical scale is the scale of the representative voxel (REV) of the earth-rock mixture. Theoretically, only when the rock-soil mass model reaches the REV scale, the parameters obtained by the relevant numerical analysis or experiment have equivalence with the parameters of the macroscopic soil-rock mixture, and the mechanical properties of the actual soil-rock mixture can be reflected, wherein the REV scale is the minimum scale of the rock mass when the mechanical parameters of the rock mass are kept basically stable. At present, researchers mostly adopt a method of numerical model of earth-rock mixture based on cellular automatic simulation for research on REV scale of earth-rock mixture, and also adopt a method of constructing a random numerical model according to statistical results of on-site rock-soil body components.
Digital image technology (DIP) is a technology that converts a digital image into a digital form and extracts important information thereof through various mathematical algorithms. This technology has been widely used in engineering, computer science, information science, statistics, physics, and other disciplines, and has a wide prospect of development. The geotechnical material is a typical inhomogeneous material with a complicated microscopic structure, and heterogeneity or microscopic information inside the geotechnical material can be extracted in a large amount by applying a digital image technology. In the last two decades, literature investigations have shown that the computational methods of fused digital image techniques are very effective for the analysis of such inhomogeneous materials.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a method for determining the scale of a soil-rock mixture characterization voxel based on a digital image technology, aiming at the problem of how to determine the scale of the soil-rock mixture characterization voxel.
The technical scheme is as follows: the invention provides a method for determining a soil-rock mixture characterization voxel scale based on a digital image technology, which comprises the following steps:
step 1: acquiring a color digital image graph of an earth-rock mixture with a large enough actual size;
the color digital image file of the earth-rock mixture in the step 1 requires that the actual size is large enough (the actual size of the graph is × mm, L)x×Ly) Digital image file formats obtained by digital cameras and the like mainly include JEPG, PNG, and bmpx×LyThe image has M × N pixel points, and the actual length corresponding to the x-direction unit pixel point is Sx＝LxThe actual length corresponding to the y-direction unit pixel point is Sy＝Ly/M。
Step 2: denoising and binarizing the obtained color digital image to obtain a binary image representing the soil and the stone, and specifically comprises the following steps:
(2.1) carrying out median filtering and denoising on the RGB color image, specifically, respectively carrying out median filtering on images corresponding to the R, G, B channels, and then fusing the processed results;
(2.2) the color image is converted from the RGB space to the HIS space, although the RGB color system is very commonly used in daily life, the RGB color system is not beneficial to image recognition by a computer program, and the HIS color system which is convenient for a computer to carry out color recognition and operation is adopted in the invention. Because the digital image has no obvious difference between soil and stone in the aspects of hue (H) and brightness (I), and the digital image can obviously distinguish the soil and the stone by adopting saturation (S), the image under the saturation (S) is selected;
and (2.3) setting a binarization threshold according to the frequency distribution condition of the S component of the image in the HIS space, and binarizing the image, specifically, setting a saturation (S) value corresponding to the maximum frequency of the occurrence of the saturation (S) value as the threshold of the binarization of the digital image according to a frequency distribution histogram of the saturation (S) value in the HIS color space.
And (2.4) in parts where the earth and the stone are not obviously distinguished (for example, the rock surface can be covered by soil sometimes), manually optimizing the earth and stone boundary in the binary image by observing the rock and soil distribution condition in the original color image to obtain the binary image of the earth and stone distribution which is more consistent with the actual condition, and using drawing software to assist in modification.
And step 3: determining m size grades according to the size of the original image, and selecting the image with the same stone content and the side length of L under each size grade as the original binary imageiObtaining m × n binary images with different sizes, and specifically comprising the following steps:
(3.1) selecting the size class LiI is more than or equal to 1 and less than or equal to m, wherein L is more than 01＜L2＜…＜Lm＜L，L＝min(Lx,Ly)，LxIs the length of the original image, LyIs a version of the original image, L1As small as possible, LmIs as close to L as possible;
(3.2) at each size level LiSelecting the image with the same stone content as the original binary image and the side length of LiN square patterns.
And 4, step 4: extracting graph boundaries from all the binary images obtained in the step 3, and converting the boundary images into vector graphs, which specifically comprises the following steps:
(4.1) extracting a pixel point set representing one of rock or soil in the binary image by using a 4-connected domain marking method;
(4.2) splitting each point belonging to the set into four lines according to the position, and recording the coordinates of the four lines;
(4.3) calculating the set of all lines, and deleting the superposed lines to obtain an outer frame of the pixel point set;
(4.4) sequencing the outer frames to form end-to-end zigzag boundaries;
(4.5) smoothing the jagged boundary;
and (4.6) converting the boundary image file into a vector graphic file according to the actual size of one pixel point.
And 5: dividing the vector graph obtained in the step 4 into finite element grids, converting the grid images into a numerical calculation model for numerical test to obtain corresponding equivalent macroscopic mechanical parameters, and specifically comprising the following steps:
(5.1) carrying out meshing by using finite element meshing software (such as Ansys, HyperWorks, Gmsh and the like);
and (5.2) simulating geotechnical mechanical experiments such as uniaxial compression test, triaxial compression test, direct shear test and the like (wherein the triaxial compression test is described as a biaxial compression test under the condition of two dimensions) by a numerical method aiming at each graph respectively to obtain equivalent macroscopic mechanical parameters corresponding to each graph, such as equivalent bonding force, equivalent internal friction angle, equivalent tangent elastic modulus, equivalent average elastic modulus and the like.
(6) And (3) sorting the numerical test results obtained in the step (5), determining the REV scale of the soil-rock mixture according to the precision requirement, and specifically comprising the following steps:
(6.1) calculating the mean value and the Coefficient of Variation (CV) value of the equivalent macroscopic mechanical parameters under each size grade, taking the equivalent elastic modulus as the equivalent macroscopic parameter to be solved as an example, and taking the size grade L as the size gradeiThe calculation formula of the lower mean value is:
size class LiThe following Coefficient of Variation (CV) values were calculated:
wherein E isi(i ═ 1,2, …, m) denotes the average of the mean modulus of elasticity for the ith size scale, Eij(i 1,2, …, m, j 1,2, …, n) represents the average modulus of elasticity of the jth image of the ith size class obtained after the three-axis compression numerical simulation, m represents the number of size classes, n represents the number of images selected for each size class,representing the standard deviation of n equivalent elastic modulus at the ith size level;
and (6.2) selecting the size corresponding to a certain CV value smaller than 15 percent according to the precision requirement, namely the REV scale of the soil-rock mixture.
Has the advantages that: compared with the existing research method, the method introduces the digital image technology, overcomes the defects that the traditional method for automatically simulating the soil-rock mixture based on the cellular cannot express the boundary shape of the rock particles and the traditional random model cannot reflect the actual soil-rock distribution situation on site, fully considers the characteristics of the soil-rock mixture on site in the REV scale of the soil-rock mixture obtained by the method, and has higher precision.
Drawings
FIG. 1 is an overall flow diagram of an embodiment of the present invention;
FIG. 2 is a gray scale image of an original color photograph of an earth and rock mixture processed in accordance with an exemplary embodiment of the present invention;
FIG. 3 is a graph of the binarization results of FIG. 2 at the S component;
FIG. 4 is a schematic illustration of selected 5 size levels;
FIG. 5 is three randomly acquired images with a size scale of 60 mm;
FIG. 6 is a graph of the results of finite element meshing of an image of size class 60 mm;
FIG. 7 is a schematic diagram of the biaxial compression test performed on FIG. 6;
FIG. 8 is a plot of mean modulus of elasticity/standard deviation versus size rating for exemplary embodiments of the present invention.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.
As shown in figure 1, the invention discloses a method for determining the dimension of a soil-rock mixture characterization body based on a digital image technology, which is specifically illustrated by taking a picture (as figure 2) of a soil-rock mixture taken from a certain project as an example,
1. and denoising and binarizing the digital image.
This photo is 200mm long, has 1000 pixel points, and wide 127mm has 636 pixel points, and the actual size that a pixel point corresponds is 0.2mm/pix for S.
2. And denoising and binarizing the color digital image.
The method comprises the steps of denoising an image by using a median filtering method, converting the digital image into an HIS color space by using an MATLAB software writing program, setting a saturation (S) value corresponding to the maximum frequency of the image to be 0.35 according to a frequency distribution histogram of the saturation (S) value of the image in an HSI color space as a binarization threshold value of the digital image, generating a binarized image, and performing auxiliary modification by using drawing software (such as Photoshop or Painter) to obtain a final binarization image of the image under an S component, wherein the final binarization image is shown in figure 3.
3. And acquiring binary graphic files with different sizes.
As shown in fig. 4, the number of selected size classes is 5, and the size classes are 20mm, 40mm, 60mm, 80mm, 100 mm. Reversing the colors of the image soil and the stone, calculating the stone content of the binary image to be about 30%, extracting 3 square image files with the stone content of 30% and each size grade as the side length under each size grade, wherein the total number of the 15 binary image files is as shown in fig. 5.
4. And extracting the graph boundary in the binary image and converting the graph boundary into a vector graph file.
And obtaining an image of a sawtooth boundary by adopting a 4-element communication marking method, processing the image boundary by adopting a mature image boundary smoothing algorithm, and converting a boundary image file into a vector image file according to the actual size of one pixel point.
5. And (4) carrying out finite element meshing and carrying out numerical test.
As shown in FIG. 7, the boundary conditions of the numerical test are the same as those of the indoor biaxial compression test, the confining pressure in the x direction is 0.5MPa, and the displacement loading mode in the y direction is 5 × 10-7And loading at the rate of m/step to obtain a stress-strain curve corresponding to each graph, and calculating the average elastic modulus according to the curve.
6. And (5) sorting the numerical test results to determine the REV scale.
The average values of the average elastic modulus and the coefficient of variation values for each size scale are shown in Table 1, and the standard deviation values for each size scale are shown in FIG. 8. If the variation coefficient value of the precision requirement is 5%, the REV scale of the soil-rock mixture is 100 mm.
TABLE 1 mean modulus of elasticity and coefficient of variation for each size class
Claims (8)
1. A method for determining the dimension of a soil-rock mixture characterization unit body based on a digital image technology is characterized by comprising the following steps:
(1) acquiring a color digital image of an earth-rock mixture with a large enough actual size;
(2) denoising and binaryzation processing are carried out on the obtained color digital image to obtain a binary image representing the soil body and the stone block;
(3) determining m size grades according to the size of the original image, and selecting the image with the same stone content and the side length of L under each size grade as the original binary imageiObtaining m × n binary images with different sizes;
(4) extracting graph boundaries from all the binary images obtained in the step 3, and converting the boundary images into vector graphs;
(5) dividing the vector graph obtained in the step (4) into finite element grids, converting the grid images into a numerical calculation model, and performing numerical test to obtain corresponding equivalent macroscopic mechanical parameters;
(6) and (5) sorting the numerical test results obtained in the step (5), and determining the REV scale of the soil-rock mixture according to the precision requirement.
2. The method for determining the earth-rock mixture characterization unit volume scale based on the digital image technology as claimed in claim 1, wherein the step 2 specifically comprises the steps of:
(2.1) carrying out median filtering denoising on the RGB color image;
(2.2) converting the color image from an RGB space to an HIS space;
(2.3) setting a binarization threshold value according to the frequency distribution condition of the S component of the image in the HIS space, and binarizing the image;
and (2.4) manually optimizing the earth and stone boundary in the binary image by observing the rock and stone distribution condition in the original color image at the part where the earth and stone are not obviously distinguished, so as to obtain the binary image of the earth and stone distribution which is more consistent with the actual condition, and using drawing software to assist in modification.
3. The method for determining the earth-rock mixture characterization unit volume scale based on the digital image technology as claimed in claim 2, wherein the method for determining the image binarization threshold in the step 2.3 specifically comprises: and setting the saturation value corresponding to the maximum occurrence frequency of the saturation value as a threshold value of image binarization according to a frequency distribution histogram of the saturation value in the HIS color space.
4. The method for determining the earth-rock mixture characterization unit volume scale based on the digital image technology as claimed in claim 1, wherein the step 3 specifically comprises the steps of:
(3.1) selecting the size class LiI is more than or equal to 1 and less than or equal to m, wherein L is more than 01＜L2＜…＜Lm＜L，L＝min(Lx,Ly)，LxIs the length of the original image, LyIs a version of the original image, L1As small as possible, LmIs as close to L as possible;
(3.2) at each size level LiSelecting the image with the same stone content as the original binary image and the side length of LiN square patterns.
5. The method for determining the earth-rock mixture characterization unit volume scale based on the digital image technology as claimed in claim 1, wherein the step 4 specifically comprises the steps of:
(4.1) extracting a pixel point set representing one of rock or soil in the binary image by using a 4-connected domain marking method;
(4.2) splitting each point belonging to the set into four lines according to the position, and recording the coordinates of the four lines;
(4.3) calculating the set of all lines, and deleting the superposed lines to obtain an outer frame of the pixel point set;
(4.4) sequencing the outer frames to form end-to-end zigzag boundaries;
(4.5) smoothing the jagged boundary;
and (4.6) converting the boundary image file into a vector graphic file according to the actual size of one pixel point.
6. The method for determining the earth-rock mixture characterization unit volume scale based on the digital image technology as claimed in claim 1, wherein the step 5 specifically comprises the steps of:
(5.1) meshing by using finite element meshing software;
and (5.2) simulating geotechnical experiments such as uniaxial compression test, triaxial compression test, direct shear test and the like by a numerical method aiming at each graph to obtain equivalent macroscopic mechanical parameters such as equivalent bonding force, equivalent internal friction angle, equivalent tangent elastic modulus, equivalent average elastic modulus and the like corresponding to each graph.
7. The method for determining the earth-rock mixture characterization unit volume scale based on the digital image technology as claimed in claim 1, wherein the step 6 specifically comprises the following steps:
(6.1) calculating the mean value and the coefficient of variation value of the equivalent macroscopic mechanical parameters at each size grade;
and (6.2) selecting the size corresponding to a certain CV value smaller than 15 percent according to the precision requirement, namely the REV scale of the soil-rock mixture.
8. The method for determining the dimension of the earth-rock mixture characterization unit volume based on the digital image technology as claimed in claim 7, wherein the equivalent macroscopic mechanical parameter is the equivalent elastic modulus, and the dimension grade L isiThe calculation formula of the lower mean value is:
wherein E isi(i ═ 1,2, …, m) denotes the average of the mean modulus of elasticity for the ith size scale, Eij(i ═ 1,2, …, m, j ═ 1,2, …, n) represents the average modulus of elasticity obtained after triaxial compression numerical simulation for the jth image of the ith size class, m represents the number of size classes, and n represents the number of images selected for each size class;
size class LiThe following formula for calculating the coefficient of variation values is:
wherein,the standard deviation of the n equivalent elastic modulus at the ith size level is shown.
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| CN111325840A (en) * | 2020-02-13 | 2020-06-23 | 中铁二院工程集团有限责任公司 | Design method and calculation system of waste slag yard |
| CN117147552A (en) * | 2023-10-30 | 2023-12-01 | 北京交通大学 | Rock slag grading analysis method for TBM tunnel |
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| CN106706393A (en) * | 2017-02-22 | 2017-05-24 | 河海大学 | Method for determining representative elementary volume (REV) of columnar jointed rock mass through similarity model |
| CN107314957A (en) * | 2017-06-30 | 2017-11-03 | 长安大学 | A kind of measuring method of rock fragmentation Size Distribution |
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| CN109359416B (en) * | 2018-11-07 | 2023-10-24 | 河北工业大学 | Particle flow numerical simulation method for reflecting real engineering earth-stone mixture distribution |
| CN111325840A (en) * | 2020-02-13 | 2020-06-23 | 中铁二院工程集团有限责任公司 | Design method and calculation system of waste slag yard |
| CN117147552A (en) * | 2023-10-30 | 2023-12-01 | 北京交通大学 | Rock slag grading analysis method for TBM tunnel |
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| CN105654482B (en) | 2019-04-30 |
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